DATA: Finding Ground-based Radars in SAR images

Synthetic Aperture Radar (SAR) satellite images are used increasingly more for Earth observation. While SAR images are useable in most conditions, they occasionally experience image degradation due to interfering signals from external radars, called Radio Frequency Interference (RFI). RFI affected images are often discarded in further analysis or pre-processed to remove the RFI. However, few on-ground radars can cause RFI in SAR images and such information can thus increase domain awareness greatly over both land and sea, where, e.g., localizing and characterizing RFI signals in the ocean could help classify otherwise overlooked ships. The aim of the current study is to detect and localize RFI signals automatically in Sentinel-1 level- 1 images and further characterize the on-ground radar. The spatial structure of RFI signals vary greatly. A convolutional autoencoder was therefore developed to reconstruct RFI-free Sentinel-1 images. Conversely, RFI-affected images could not be well reconstructed. Anomalous heatmaps were then developed to automatically detect and localize RFI anomalies in the im- ages under varying environmental and geographical conditions whereafter the external radar characteristics were extracted manually from Sentinel-1 level-0 data. We could consequently classify and localize RFI signals believed to originate from both stationary radars and ship-borne radars. We further argue that the calculated ship-borne radar characteristics correspond to those of air-surveillance radars. Empirically, the method showed better detection results than those of previous studies. Our study shows that more information can be extracted from certain detected objects, such as ships, from SAR images.

合成孔径雷达（SAR）卫星图像在地球观测中的应用日益广泛。尽管SAR图像在大多数条件下均可使用，但偶尔会因外部雷达的干扰信号（称为射频干扰，RFI）而遭遇图像退化。受RFI影响的图像通常在进一步分析或预处理中被舍弃或去除RFI。然而，地面上的少数雷达可以产生RFI，此类信息因此可极大地提升陆地和海洋的领域认知，例如，在海洋中定位和表征RFI信号有助于识别其他情况下被忽视的船只。当前研究的目的是在Sentinel-1级别1图像中自动检测和定位RFI信号，并进一步表征地面雷达。RFI信号的时空结构差异很大。因此，开发了一种卷积自编码器以重建无RFI的Sentinel-1图像。相反，受RFI影响的图像无法得到良好重建。随后，开发了异常热图以自动检测和定位在多变的环境和地理条件下图像中的RFI异常，然后从Sentinel-1级别0数据中手动提取外部雷达的特性。据此，我们能够对源自固定雷达和船载雷达的RFI信号进行分类和定位。我们进一步认为，计算出的船载雷达特性与空中监视雷达的特性相符。经验表明，该方法在检测结果上优于以往的研究。我们的研究表明，可以从某些检测到的对象，如船只，中提取更多信息，这些信息来自SAR图像。